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reverse-engineering逆向工程

Agent Skill

reverse-engineering 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

23,076

周安装

952

GitHub Stars

3

下载量

7,540
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reverse-engineering(逆向工程)
来源仓库:https://github.com/ivangdavila/reverse-engineering
安装命令:
openclaw skills install reverse-engineering
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install reverse-engineering

简介

使用证据梯、接口图和可证伪的假设对二进制文件、API、协议和工作流程进行逆向工程。

SKILL.md

name
Reverse Engineering
slug
reverse-engineering
version
1.0.0
homepage
https://clawic.com/skills/reverse-engineering
description
Reverse engineer binaries, APIs, protocols, and workflows with evidence ladders, interface maps, and falsifiable hypotheses.
changelog
Adds a structured reverse engineering workflow with evidence tracking, interface mapping, and safer uncertainty handling.
metadata
{"clawdbot":{"emoji":"🧩","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/reverse-engineering/"]}}

When to Use

User needs to understand something opaque, undocumented, legacy, or partially broken. Agent handles behavioral tracing, artifact mapping, hypothesis testing, and concise documentation for binaries, APIs, file formats, protocols, devices, and human workflows.

Architecture

Memory lives in ~/reverse-engineering/. If ~/reverse-engineering/ does not exist, run setup.md. See memory-template.md for structure.

~/reverse-engineering/
├── memory.md          # durable preferences, approvals, and common target types
├── current-target.md  # active engagement snapshot
├── targets/           # one file per target or system
└── artifacts/         # traces, decoded notes, and reproduction snippets

Quick Reference

Use these files on demand instead of loading the whole method every time.

TopicFile
Setup guidesetup.md
Memory templatememory-template.md
TRACE protocolprotocol.md
Confidence modelevidence-ladder.md
Surface mappinginterface-map.md
Deliverable templatesdeliverables.md
Safety boundariesboundaries.md

Requirements

  • Authorized access to the target, samples, and environment being analyzed
  • A clear statement of whether the target is production, staging, or an offline copy
  • Explicit user approval before any invasive, destructive, or credential-bearing step

Core Rules

1. Bound the job before probing

  • Name the target, desired outcome, available artifacts, and operational boundary first.
  • Ask what is allowed: read-only inspection, replay, instrumentation, decompilation, fuzzing, or patching.
  • If the boundary is unclear, default to the safest read-only path.
  • Before the first persistent write, state what will be stored locally and ask for permission.

2. Run the TRACE loop from protocol.md

  • Triage the target.
  • Record observable behavior.
  • Abstract hypotheses.
  • Challenge each hypothesis with the smallest useful test.
  • Explain the result in user-facing language.

3. Separate evidence, inference, and guess

  • Tag every claim using the ladder in evidence-ladder.md.
  • Never blur "observed" with "likely" or "possible."
  • When certainty is low, say what would raise confidence instead of pretending to know.

4. Map surfaces before internals

  • Build the interface inventory from interface-map.md before writing an implementation story.
  • Start from inputs, outputs, states, side effects, and trust boundaries.
  • Reverse engineering is faster when the outer contract is stable before diving deeper.

5. Prefer minimal, reproducible probes

  • Use the smallest sample, trace, packet, call, or binary slice that can prove or disprove a hypothesis.
  • Keep every probe replayable and attributable.
  • If a result cannot be reproduced, it is a clue, not a conclusion.

6. Deliver models, not raw notes

  • Every session should end with concrete outputs from deliverables.md: target brief, interface map, hypothesis ledger, reproduction note, and remaining unknowns.
  • Optimize for what the user can act on next: debug, reimplement, migrate, document, or secure.
  • Good reverse engineering compresses complexity without hiding uncertainty.

Common Traps

These failures usually waste the most time or create false confidence.

TrapWhy It FailsBetter Move
Jumping straight to decompilationYou lose the external contract and contextStart with behavior, interfaces, and captured artifacts
Treating logs as truthLogs reflect one code path and one viewpointCross-check with traces, outputs, and controlled inputs
Reverse engineering the whole systemScope explodes and confidence dropsPick one question, one layer, and one target surface
Confusing correlation with mechanismSimilar timings or names can misleadDesign a falsifiable probe before concluding
Keeping findings in loose notesKnowledge becomes untestable and unreusableConvert findings into deliverables with evidence tags
Poking live systems casuallyYou create risk and destroy signalPrefer offline copies, captures, and explicit approvals

Security & Privacy

Data that leaves your machine:

  • Nothing by default.
  • Only user-approved samples or public documentation if the task explicitly requires external lookup.

Data that stays local:

  • Preferences and engagement notes in ~/reverse-engineering/
  • Captured traces, decoded notes, and reproduction snippets kept in the workspace or the local reverse-engineering folder

This skill does NOT:

  • Steal credentials, bypass authorization, or hide activity
  • Run exploit chains on production targets by default
  • Claim certainty without evidence
  • Persist sensitive data outside the documented local folder
  • Create durable local memory without first telling the user what will be stored

Related Skills

Install with clawhub install <slug> if user confirms:

  • analysis — structure ambiguous problems and turn raw evidence into decisions
  • api — reason about endpoints, payloads, contracts, and integration behavior
  • architecture — model system boundaries, components, and data flow once the target is understood
  • bash — build small inspection and replay loops for traces, logs, and artifacts
  • cybersecurity — evaluate trust boundaries, attack surface, and safe handling of sensitive targets

Feedback

  • If useful: clawhub star reverse-engineering
  • Stay updated: clawhub sync

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

79.24%
按下载量换算5,975

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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